TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

The article announces the release of TaCarla, a comprehensive benchmarking dataset specifically designed for end-to-end autonomous driving, comprising over 2.85 million frames collected using the CARLA simulation environment. This dataset addresses gaps in existing benchmarks by integrating perception and planning tasks, supporting dynamic object detection, lane divider detection, and traffic light recognition, while also providing numerical rarity scores for better evaluation of model performance. TaCarla's diverse scenarios and closed-loop evaluation capabilities make it a significant resource for practitioners aiming to enhance the robustness and performance of autonomous driving systems.